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[Udemy] A Complete Guide on TensorFlow 2.0 using Keras API (2021) [En]

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文件列表

  1. 16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.mp4 194MB
  2. 17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.mp4 187MB
  3. 1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4 146MB
  4. 16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.mp4 140MB
  5. 7. Deep Reinforcement Learning Theory/9. Action Selection Policies.mp4 137MB
  6. 17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.mp4 136MB
  7. 17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.mp4 121MB
  8. 16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.mp4 118MB
  9. 2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.mp4 115MB
  10. 7. Deep Reinforcement Learning Theory/8. Experience Replay.mp4 115MB
  11. 15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.mp4 112MB
  12. 17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.mp4 111MB
  13. 16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.mp4 108MB
  14. 7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.mp4 100MB
  15. 15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.mp4 99MB
  16. 16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.mp4 98MB
  17. 7. Deep Reinforcement Learning Theory/5. Temporal Difference.mp4 97MB
  18. 7. Deep Reinforcement Learning Theory/2. The Bellman Equation.mp4 95MB
  19. 7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).mp4 94MB
  20. 4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.mp4 88MB
  21. 15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.mp4 82MB
  22. 7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.mp4 79MB
  23. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.mp4 74MB
  24. 2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.mp4 71MB
  25. 7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.mp4 69MB
  26. 15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.mp4 67MB
  27. 3. Artificial Neural Networks/2. Data Preprocessing.mp4 62MB
  28. 15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.mp4 61MB
  29. 3. Artificial Neural Networks/3. Building the Artificial Neural Network.mp4 60MB
  30. 3. Artificial Neural Networks/1. Project Setup.mp4 59MB
  31. 4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.mp4 58MB
  32. 8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.mp4 54MB
  33. 16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.mp4 53MB
  34. 11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.mp4 53MB
  35. 6. Transfer Learning and Fine Tuning/2. Project Setup.mp4 49MB
  36. 2. TensorFlow 2.0 Basics/3. Operations with Tensors.mp4 49MB
  37. 5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.mp4 49MB
  38. 3. Artificial Neural Networks/4. Training the Artificial Neural Network.mp4 49MB
  39. 4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.mp4 47MB
  40. 6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.mp4 46MB
  41. 5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.mp4 46MB
  42. 15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.mp4 45MB
  43. 15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.mp4 43MB
  44. 7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.mp4 43MB
  45. 2. TensorFlow 2.0 Basics/4. Strings.mp4 40MB
  46. 5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.mp4 40MB
  47. 8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.mp4 39MB
  48. 11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.mp4 37MB
  49. 11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.mp4 35MB
  50. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.mp4 35MB
  51. 8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.mp4 33MB
  52. 6. Transfer Learning and Fine Tuning/9. Image Data Generators.mp4 33MB
  53. 8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.mp4 32MB
  54. 6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.mp4 32MB
  55. 3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.mp4 31MB
  56. 16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.mp4 30MB
  57. 13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.mp4 29MB
  58. 14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.mp4 28MB
  59. 8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.mp4 28MB
  60. 12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.mp4 28MB
  61. 11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.mp4 28MB
  62. 12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.mp4 27MB
  63. 8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.mp4 27MB
  64. 14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.mp4 26MB
  65. 12. Image Classification API with TensorFlow Serving/3. Project setup.mp4 26MB
  66. 12. Image Classification API with TensorFlow Serving/6. Saving the model for production.mp4 25MB
  67. 9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.mp4 25MB
  68. 6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.mp4 25MB
  69. 12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.mp4 24MB
  70. 9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.mp4 24MB
  71. 9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.mp4 24MB
  72. 12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.mp4 24MB
  73. 12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.mp4 24MB
  74. 12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.mp4 23MB
  75. 9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.mp4 22MB
  76. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.mp4 21MB
  77. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.mp4 21MB
  78. 11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.mp4 20MB
  79. 17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.mp4 20MB
  80. 9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.mp4 20MB
  81. 6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.mp4 20MB
  82. 12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.mp4 20MB
  83. 6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.mp4 18MB
  84. 6. Transfer Learning and Fine Tuning/10. Transfer Learning.mp4 17MB
  85. 8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.mp4 16MB
  86. 8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.mp4 16MB
  87. 16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.mp4 16MB
  88. 13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.mp4 15MB
  89. 13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.mp4 15MB
  90. 14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).mp4 14MB
  91. 13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.mp4 14MB
  92. 6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.mp4 13MB
  93. 6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.mp4 13MB
  94. 14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.mp4 12MB
  95. 11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.mp4 12MB
  96. 11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.mp4 12MB
  97. 8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.mp4 12MB
  98. 8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.mp4 12MB
  99. 8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.mp4 12MB
  100. 15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.mp4 12MB
  101. 14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.mp4 11MB
  102. 17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.mp4 10MB
  103. 6. Transfer Learning and Fine Tuning/14. Fine Tuning.mp4 10MB
  104. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.mp4 10MB
  105. 8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.mp4 10MB
  106. 12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.mp4 10MB
  107. 13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.mp4 9MB
  108. 6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.mp4 9MB
  109. 14. Distributed Training with TensorFlow 2.0/2. Project Setup.mp4 9MB
  110. 6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.mp4 9MB
  111. 13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.mp4 9MB
  112. 9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.mp4 8MB
  113. 13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.mp4 8MB
  114. 16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.mp4 8MB
  115. 14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.mp4 7MB
  116. 6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.mp4 6MB
  117. 13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.mp4 6MB
  118. 6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.mp4 6MB
  119. 13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.mp4 5MB
  120. 9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.mp4 2MB
  121. 11. Fashion API with Flask and TensorFlow 2.0/1.1 Flask API.zip 372KB
  122. 9. Data Validation with TensorFlow Data Validation (TFDV)/1.2 pollution_small.csv 73KB
  123. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1.2 pollution_small.csv 73KB
  124. 7. Deep Reinforcement Learning Theory/2. The Bellman Equation.srt 31KB
  125. 7. Deep Reinforcement Learning Theory/5. Temporal Difference.srt 29KB
  126. 16. Annex 2 - Convolutional Neural Networks Theory/7. Step 4 - Full Connection.srt 29KB
  127. 17. Annex 3 - Recurrent Neural Networks Theory/4. LSTMs.srt 28KB
  128. 7. Deep Reinforcement Learning Theory/3. Markov Decision Process (MDP).srt 27KB
  129. 1. Introduction/1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..srt 26KB
  130. 16. Annex 2 - Convolutional Neural Networks Theory/9. Softmax & Cross-Entropy.srt 26KB
  131. 15. Annex 1 - Artificial Neural Networks Theory/2. The Neuron.srt 25KB
  132. 17. Annex 3 - Recurrent Neural Networks Theory/2. What are Recurrent Neural Networks.srt 24KB
  133. 7. Deep Reinforcement Learning Theory/9. Action Selection Policies.srt 24KB
  134. 7. Deep Reinforcement Learning Theory/8. Experience Replay.srt 24KB
  135. 16. Annex 2 - Convolutional Neural Networks Theory/3. Step 1 - Convolution.srt 23KB
  136. 7. Deep Reinforcement Learning Theory/6. Deep Q-Learning Intuition - Step 1.srt 22KB
  137. 16. Annex 2 - Convolutional Neural Networks Theory/2. What are Convolutional Neural Networks.srt 22KB
  138. 7. Deep Reinforcement Learning Theory/4. Q-Learning Intuition.srt 22KB
  139. 17. Annex 3 - Recurrent Neural Networks Theory/5. LSTM Practical Intuition.srt 21KB
  140. 16. Annex 2 - Convolutional Neural Networks Theory/5. Step 2 - Max Pooling.srt 21KB
  141. 17. Annex 3 - Recurrent Neural Networks Theory/3. Vanishing Gradient.srt 21KB
  142. 4. Convolutional Neural Networks/2. Building the Convolutional Neural Network.srt 20KB
  143. 15. Annex 1 - Artificial Neural Networks Theory/5. How do Neural Networks Learn.srt 19KB
  144. 15. Annex 1 - Artificial Neural Networks Theory/4. How do Neural Networks Work.srt 19KB
  145. 7. Deep Reinforcement Learning Theory/1. What is Reinforcement Learning.srt 18KB
  146. 2. TensorFlow 2.0 Basics/1. From TensorFlow 1.x to TensorFlow 2.0.srt 17KB
  147. 3. Artificial Neural Networks/3. Building the Artificial Neural Network.srt 15KB
  148. 15. Annex 1 - Artificial Neural Networks Theory/6. Gradient Descent.srt 14KB
  149. 2. TensorFlow 2.0 Basics/2. Constants, Variables, Tensors.srt 13KB
  150. 15. Annex 1 - Artificial Neural Networks Theory/7. Stochastic Gradient Descent.srt 12KB
  151. 15. Annex 1 - Artificial Neural Networks Theory/3. The Activation Function.srt 12KB
  152. 4. Convolutional Neural Networks/1. Project Setup & Data Preprocessing.srt 11KB
  153. 4. Convolutional Neural Networks/3. Training and Evaluating the Convolutional Neural Network.srt 11KB
  154. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/5. Dataset preprocessing pipeline.srt 11KB
  155. 3. Artificial Neural Networks/2. Data Preprocessing.srt 11KB
  156. 3. Artificial Neural Networks/4. Training the Artificial Neural Network.srt 10KB
  157. 5. Recurrent Neural Networks/3. Training and Evaluating the Recurrent Neural Network.srt 10KB
  158. 5. Recurrent Neural Networks/1. Project Setup & Data Preprocessing.srt 10KB
  159. 7. Deep Reinforcement Learning Theory/7. Deep Q-Learning Intuition - Step 2.srt 10KB
  160. 16. Annex 2 - Convolutional Neural Networks Theory/4. Step 1 Bis - ReLU Layer.srt 10KB
  161. 8. Deep Reinforcement Learning for Stock Market trading/12. Training loop - Step 2.srt 10KB
  162. 8. Deep Reinforcement Learning for Stock Market trading/8. State creator function.srt 10KB
  163. 5. Recurrent Neural Networks/2. Building the Recurrent Neural Network.srt 9KB
  164. 2. TensorFlow 2.0 Basics/4. Strings.srt 9KB
  165. 3. Artificial Neural Networks/1. Project Setup.srt 9KB
  166. 2. TensorFlow 2.0 Basics/3. Operations with Tensors.srt 8KB
  167. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/2. Initial dataset preprocessing.srt 8KB
  168. 8. Deep Reinforcement Learning for Stock Market trading/7. Dataset Loader function.srt 8KB
  169. 8. Deep Reinforcement Learning for Stock Market trading/2. AI Trader - Step 1.srt 8KB
  170. 11. Fashion API with Flask and TensorFlow 2.0/1. Project Setup.srt 8KB
  171. 12. Image Classification API with TensorFlow Serving/1. What is the TensorFlow Serving.srt 8KB
  172. 15. Annex 1 - Artificial Neural Networks Theory/8. Backpropagation.srt 7KB
  173. 12. Image Classification API with TensorFlow Serving/9. Sending the first POST request to the model.srt 7KB
  174. 8. Deep Reinforcement Learning for Stock Market trading/11. Training loop - Step 1.srt 7KB
  175. 3. Artificial Neural Networks/5. Evaluating the Artificial Neural Network.srt 7KB
  176. 9. Data Validation with TensorFlow Data Validation (TFDV)/3. Creating dataset Schema.srt 6KB
  177. 6. Transfer Learning and Fine Tuning/3. Dataset preprocessing.srt 6KB
  178. 6. Transfer Learning and Fine Tuning/9. Image Data Generators.srt 6KB
  179. 8. Deep Reinforcement Learning for Stock Market trading/6. AI Trader - Step 5.srt 6KB
  180. 6. Transfer Learning and Fine Tuning/1. What is Transfer Learning.srt 6KB
  181. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/4. Preprocessing function.srt 6KB
  182. 16. Annex 2 - Convolutional Neural Networks Theory/8. Summary.srt 6KB
  183. 11. Fashion API with Flask and TensorFlow 2.0/5. Creating classify function.srt 6KB
  184. 14. Distributed Training with TensorFlow 2.0/3. Dataset preprocessing.srt 5KB
  185. 9. Data Validation with TensorFlow Data Validation (TFDV)/5. Anomaly detection with TensorFlow Data Validation.srt 5KB
  186. 16. Annex 2 - Convolutional Neural Networks Theory/1. Plan of Attack.srt 5KB
  187. 12. Image Classification API with TensorFlow Serving/6. Saving the model for production.srt 5KB
  188. 13. TensorFlow Lite Prepare a model for a mobile device/1. What is the TensorFlow Lite.srt 5KB
  189. 12. Image Classification API with TensorFlow Serving/4. Dataset preprocessing.srt 5KB
  190. 9. Data Validation with TensorFlow Data Validation (TFDV)/2. Loading the pollution dataset.srt 5KB
  191. 12. Image Classification API with TensorFlow Serving/7. Serving the TensorFlow 2.0 Model.srt 5KB
  192. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/3. Dataset metadata.srt 5KB
  193. 9. Data Validation with TensorFlow Data Validation (TFDV)/1. Project Setup.srt 5KB
  194. 12. Image Classification API with TensorFlow Serving/3. Project setup.srt 5KB
  195. 17. Annex 3 - Recurrent Neural Networks Theory/6. LSTM Variations.srt 5KB
  196. 6. Transfer Learning and Fine Tuning/12. Fine Tuning model definition.srt 5KB
  197. 6. Transfer Learning and Fine Tuning/2. Project Setup.srt 5KB
  198. 12. Image Classification API with TensorFlow Serving/2. TensorFlow Serving architecture.srt 4KB
  199. 11. Fashion API with Flask and TensorFlow 2.0/7. Sending API requests over internet to the model.srt 4KB
  200. 14. Distributed Training with TensorFlow 2.0/7. Final evaluation - Speed test normal model vs distributed model.srt 4KB
  201. 14. Distributed Training with TensorFlow 2.0/1. What is the Distributed Training.srt 4KB
  202. 9. Data Validation with TensorFlow Data Validation (TFDV)/6. Preparing Schema for production.srt 4KB
  203. 13. TensorFlow Lite Prepare a model for a mobile device/3. Dataset preprocessing.srt 4KB
  204. 6. Transfer Learning and Fine Tuning/6. Adding a custom head to the pre-trained model.srt 4KB
  205. 15. Annex 1 - Artificial Neural Networks Theory/1. Plan of Attack.srt 4KB
  206. 11. Fashion API with Flask and TensorFlow 2.0/3. Loading a pre-trained model.srt 4KB
  207. 6. Transfer Learning and Fine Tuning/4. Loading the MobileNet V2 model.srt 4KB
  208. 14. Distributed Training with TensorFlow 2.0/4. Defining a non-distributed model (normal CNN model).srt 4KB
  209. 17. Annex 3 - Recurrent Neural Networks Theory/1. Plan of Attack.srt 4KB
  210. 6. Transfer Learning and Fine Tuning/8. Compiling the Transfer Learning model.srt 4KB
  211. 12. Image Classification API with TensorFlow Serving/8. Creating a JSON object.srt 3KB
  212. 8. Deep Reinforcement Learning for Stock Market trading/5. AI Trader - Step 4.srt 3KB
  213. 12. Image Classification API with TensorFlow Serving/5. Defining, training and evaluating a model.srt 3KB
  214. 13. TensorFlow Lite Prepare a model for a mobile device/4. Building a model.srt 3KB
  215. 8. Deep Reinforcement Learning for Stock Market trading/10. Defining the model.srt 3KB
  216. 6. Transfer Learning and Fine Tuning/10. Transfer Learning.srt 3KB
  217. 8. Deep Reinforcement Learning for Stock Market trading/4. AI Trader - Step 3.srt 3KB
  218. 8. Deep Reinforcement Learning for Stock Market trading/1. Project Setup.srt 3KB
  219. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1. Project Setup.srt 3KB
  220. 11. Fashion API with Flask and TensorFlow 2.0/6. Starting the Flask application.srt 3KB
  221. 16. Annex 2 - Convolutional Neural Networks Theory/6. Step 3 - Flattening.srt 3KB
  222. 6. Transfer Learning and Fine Tuning/14. Fine Tuning.srt 3KB
  223. 13. TensorFlow Lite Prepare a model for a mobile device/2. Project setup.srt 3KB
  224. 11. Fashion API with Flask and TensorFlow 2.0/2. Importing project dependencies.srt 3KB
  225. 8. Deep Reinforcement Learning for Stock Market trading/3. AI Trader - Step 2.srt 2KB
  226. 13. TensorFlow Lite Prepare a model for a mobile device/5. Training, evaluating the model.srt 2KB
  227. 12. Image Classification API with TensorFlow Serving/10. Sending the POST request to a specific model.srt 2KB
  228. 6. Transfer Learning and Fine Tuning/7. Defining the transfer learning model.srt 2KB
  229. 14. Distributed Training with TensorFlow 2.0/6. Defining a distributed model.srt 2KB
  230. 18. Bonus Lectures/3. FREE LEARNING RESOURCES FOR YOU.html 2KB
  231. 14. Distributed Training with TensorFlow 2.0/5. Setting up a distributed strategy.srt 2KB
  232. 13. TensorFlow Lite Prepare a model for a mobile device/10. What's next.html 2KB
  233. 13. TensorFlow Lite Prepare a model for a mobile device/6. Saving the model.srt 2KB
  234. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/6. What's next.html 2KB
  235. 9. Data Validation with TensorFlow Data Validation (TFDV)/8. What's next.html 2KB
  236. 14. Distributed Training with TensorFlow 2.0/2. Project Setup.srt 2KB
  237. 13. TensorFlow Lite Prepare a model for a mobile device/9. Saving the converted model.srt 2KB
  238. 6. Transfer Learning and Fine Tuning/15. Evaluating Fine Tuning results.srt 2KB
  239. 9. Data Validation with TensorFlow Data Validation (TFDV)/7. Saving the Schema.srt 2KB
  240. 8. Deep Reinforcement Learning for Stock Market trading/9. Loading the dataset.srt 2KB
  241. 13. TensorFlow Lite Prepare a model for a mobile device/7. TensorFlow Lite Converter.srt 2KB
  242. 11. Fashion API with Flask and TensorFlow 2.0/4. Defining the Flask application.srt 2KB
  243. 6. Transfer Learning and Fine Tuning/5. Freezing the pre-trained model.srt 2KB
  244. 6. Transfer Learning and Fine Tuning/11. Evaluating Transfer Learning results.srt 2KB
  245. 6. Transfer Learning and Fine Tuning/13. Compiling the Fine Tuning model.srt 1KB
  246. 13. TensorFlow Lite Prepare a model for a mobile device/8. Converting the model to a TensorFlow Lite model.srt 1KB
  247. 1. Introduction/4. BONUS Learning Path.html 1KB
  248. 18. Bonus Lectures/2. YOUR SPECIAL BONUS.html 1KB
  249. 9. Data Validation with TensorFlow Data Validation (TFDV)/4. Computing test set statistics.srt 840B
  250. 18. Bonus Lectures/1. SPECIAL COVID-19 BONUS.html 722B
  251. 1. Introduction/3. BONUS 10 advantages of TensorFlow.html 613B
  252. 4. Convolutional Neural Networks/6. HOMEWORK SOLUTION Convolutional Neural Networks.html 573B
  253. 4. Convolutional Neural Networks/5. HOMEWORK Convolutional Neural Networks.html 500B
  254. 3. Artificial Neural Networks/7. HOMEWORK Artificial Neural Networks.html 493B
  255. 1. Introduction/2. Course Curriculum & Colab Toolkit.html 464B
  256. 3. Artificial Neural Networks/8. HOMEWORK SOLUTION Artificial Neural Networks.html 421B
  257. 2. TensorFlow 2.0 Basics/1.1 Google Colab TensorFlow 1.x to TensorFlow 2.0.html 134B
  258. 3. Artificial Neural Networks/1.1 Google Colab ANN.html 134B
  259. 4. Convolutional Neural Networks/1.1 Google Colab CNN.html 134B
  260. 5. Recurrent Neural Networks/1.1 Google Colab RNN.html 134B
  261. 6. Transfer Learning and Fine Tuning/2.1 Google Colab Transfer Learning and Fine Tuning.html 134B
  262. 8. Deep Reinforcement Learning for Stock Market trading/1.1 Google Colab Deep-Q Trading Bot.html 134B
  263. 9. Data Validation with TensorFlow Data Validation (TFDV)/1.1 Google Colab TFDV.html 134B
  264. 10. Dataset Preprocessing with TensorFlow Transform (TFT)/1.1 Google Colab TFT.html 134B
  265. 12. Image Classification API with TensorFlow Serving/3.1 Google Colab TensorFlow Serving.html 134B
  266. 13. TensorFlow Lite Prepare a model for a mobile device/2.1 Google Colab TensorFlow Lite.html 134B
  267. 14. Distributed Training with TensorFlow 2.0/2.1 Google Colab Distributed Training.html 134B
  268. 3. Artificial Neural Networks/6. Artificial Neural Network Quiz.html 123B
  269. 4. Convolutional Neural Networks/4. Convolutional Neural Networks Quiz.html 123B
  270. 5. Recurrent Neural Networks/4. Recurrent Neural Network Quiz.html 123B
  271. 6. Transfer Learning and Fine Tuning/16. Transfer Learning quiz.html 123B
  272. 8. Deep Reinforcement Learning for Stock Market trading/8.1 Yahoo finance - APPLE stocks.html 119B